Calculated conformer energies for organic molecules with multiple polar functionalities are method dependent: Taxol (case study)

BACKGROUND: Molecular mechanics (MM) and quantum chemical (QM) calculations are widely applied and powerful tools for the stereochemical and conformational investigations of molecules. The same methods have been extensively used to probe the conformational profile of Taxol (Figure 1) both in solution and at the β-tubulin protein binding site. RESULTS: In the present work, the relative energies of seven conformations of Taxol derived from NMR and X-ray analyses were compared with a set of widely used force fields and semiempirical MO methods coupled to a continuum solvent treatment. The procedures not only diverge significantly in their assessment of relative conformational energies, but none of them provide satisfactory agreement with experiment. CONCLUSIONS: For Taxol, molecular mechanics and semiempirical QM methods are unable to provide a consistent energetic ranking of side-chain conformations. For similar highly polar organic structures, "energy-free" conformational search methods are advised

Human rhinovirus promotes STING trafficking to replication organelles to promote viral replication

Human rhinovirus (HRV), like coronavirus (HCoV), are positive-strand RNA viruses that cause both upper and lower respiratory tract illness, with their replication facilitated by concentrating RNA-synthesizing machinery in intracellular compartments made of modified host membranes, referred to as replication organelles (ROs). Here we report a non-canonical, essential function for stimulator of interferon genes (STING) during HRV infections. While the canonical function of STING is to detect cytosolic DNA and activate inflammatory responses, HRV infection triggers the release of STIM1-bound STING in the ER by lowering Ca2+, thereby allowing STING to interact with phosphatidylinositol 4-phosphate (PI4P) and traffic to ROs to facilitates viral replication and transmission via autophagy. Our results thus hint a critical function of STING in HRV viral replication and transmission, with possible implications for other RO-mediated RNA viruses

Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules

Druggability assessment of a target protein has emerged in recent years as an important concept in hit-to-lead optimization. A reliable and physically relevant measure of druggability would allow informed decisions on the risk of investing in a particular target. Here, we define “druggability” as a quantitative estimate of binding sites and affinities for a potential drug acting on a specific protein target. In the present study, we describe a new methodology that successfully predicts the druggability and maximal binding affinity for a series of challenging targets, including those that function through allosteric mechanisms. Two distinguishing features of the methodology are (i) simulation of the binding dynamics of a diversity of probe molecules selected on the basis of an analysis of approved drugs and (ii) identification of druggable sites and estimation of corresponding binding affinities on the basis of an evaluation of the geometry and energetics of bound probe clusters. The use of the methodology for a variety of targets such as murine double mutant-2, protein tyrosine phosphatase 1B (PTP1B), lymphocyte function-associated antigen 1, vertebrate kinesin-5 (Eg5), and p38 mitogen-activated protein kinase provides examples for which the method correctly captures the location and binding affinities of known drugs. It also provides insights into novel druggable sites and the target’s structural changes that would accommodate, if not promote and stabilize, drug binding. Notably, the ability to identify high affinity spots even in challenging cases such as PTP1B or Eg5 shows promise as a rational tool for assessing the druggability of protein targets and identifying allosteric or novel sites for drug binding

Automated High Throughput pKa and Distribution Coefficient Measurements of Pharmaceutical Compounds for the SAMPL8 Blind Prediction Challenge

The goal of the SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pKa and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules

Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge

The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pKa and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules